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Abstract Statistical machine learning classification methods have been widely used in the fault detection analysis in several engineering domains. This motivates us to provide in this article an overview on the application of these methods in the fault diagnosis strategies and also their successful use in unmanned aerial vehicles (UAVs) systems. Different existing aspects including the implementation conditions, offline design, and online computation algorithms as well as computation complexity and detection time are discussed in detail. Evaluation and validation of these aspects have been ensured by a simple demonstration of the basic classification methods and neural network techniques in solving the fault detection and diagnosis problem of the propulsion system failure of a multirotor UAV. A testing platform of an Hexarotor UAV is completely realized.

Abstract The civil aircraft nosewheel is clamped, lifted, and retained through the pick-up and holding system of the towbarless towing vehicle (TLTV), and the aircraft may be moved from the parking position to an adjacent one, the taxiway, a maintenance hangar, a location near the active runway, or conversely only with the power of the TLTV. The TLTV interfacing with the nose-landing gear of civil transport aircraft for the long-distance towing operations at a high speed could be defined as a towbarless aircraft taxiing system (TLATS). The dynamic loads induced by the system vibration may cause damage or reduce the certified safe-life limit of the nose-landing gear or the TLTV when the towing speed increases up to 40 km/h during the towing operations due to the maximum ramp weight of a heavy aircraft.

Abstract Numerous operational procedures regulate aerodrome ground traffic. Detailed solutions in these procedures often come from preventive recommendations formulated as a result of accident cause analysis. With time, the conclusions drawn based on incidents, i.e., events that did not result in material damage or casualties, are becoming increasingly significant. In this article, we propose a new method for determining the probability of an incident turning into an air accident, based on the example of aerodrome traffic operations. Premises conducive to an accident in the considered class of events depend on both human and physical factors. Thus a hybrid approach was applied. We used a fuzzy inference system to analyze the premises dependent on vehicle operators, while the simulation method was selected to examine the premises dependent on physical factors. Both were integrated using the technique of event trees with fuzzy probabilities (ETFP).

Abstract Laminated composites are extensively used in the aerospace industry. However, structures made from laminated composites are highly susceptible to delamination failures. It is therefore imperative to consider a structure tolerance to delamination during design and operation. Hybrid composites with laminas containing different fibers were used earlier in laminates to achieve certain benefits in strength, stiffness, and buckling. However, the concept of mixing laminas with different fibers was not explored by researchers to enhance delamination tolerance levels. This article examines the above aspect of hybridization by employing machine learning algorithms and proposes a reliable method of analysis to study delamination, which is crucial to ensure the safety of airframe composite panels.

Abstract Prediction of the surface finish of hardened bearing steels was estimated in machining with ceramic uncoated cutting tools under various process parameters using two statistical approaches. A second-order (quadratic) regression model (MQR, multiple quantile regression) for the surface finish was developed and then compared with the artificial neural network (ANN) method based on the coefficient determination (R 2), root mean square error (RMSE), and percentage error (PE). The experimental results exhibited that cutting speed was the dominant parameter, but feed rate and depth of cut were insignificant in terms of the Pareto chart and analysis of variance (ANOVA). The optimum surface finish in machining bearing steel was achieved at 100 m/min speed, 0.1 mm/revolution (rev) feed rate, and 0.6 mm depth of cut.

Abstract The main focus of this article is the online estimation of the terminal airspace sector capacity from the Air Traffic Controller 0ATC) dynamical neural model using Neural Partial Differentiation (NPD) with permissible safe separation and affordable workload. For this purpose, a primarily neural model of a multi-input-single-output (MISO) ATC dynamical system is established, and the NPD method is used to estimate the model parameters from the experimental data. These estimated parameters have a less relative standard deviation, and hence the model validation results show that the predicted neural model response is well matched with the intervention of the ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters, which are unknown in practice.

Abstract Sleep quality and maintenance of the optimal cognitive functioning is of crucial importance for aviation safety. Fatigue Risk Management (FRM) enables the operator to achieve the objectives set in their safety and FRM policies. As in any other risk management cycle, the FRM value can be realized by deploying suitable tools that aid robust decision-making. For the purposes of our article, we focus on fatigue hazard identification to explore the possible developments forward through the enhancement of objective tools in air transport operators. To this end we compare subjective and objective tools that could be employed by an FRM system. Specifically, we focus on an exploratory survey on 120 pilots and the analysis of 250 fatigue reports that are compared with objective fatigue assessment based on the polysomnographic (PSG) and neurocognitive assessment of three experimental cases.

Abstract Emissions and effects of climate change have prompted study into fuels that reduce global dependence on traditional fuels. This study seeks to investigate engine performance, thermochemical properties, emissions, and perform NVH analysis of Jet-A and S8 using a single-stage turbojet engine at three engine speeds. Experimental Jet-A results were used to validate a CFX simulation of the engine. Engine performance was quantified using thermocouples, pressure sensors, tachometers, flow meters, and load cells fitted to the engine. Emissions results were collected using an MKS Multigas Emissions Analyzer that examined CO, CO₂, H₂O, NOx, and THC. NVH analysis was conducted using a multifield, free-field microphone, and triaxial accelerometer. This study found that Jet-A operates at higher temperatures and pressures than S8, and S8 requires higher fuel flow rates than Jet-A, leading to poorer efficiency and thrust. S8 produced stronger vibrations over 5 kHz compared to Jet-A.

Erratum.

Abstract Aircraft lift bag is the equipment used for the recovery of an aircraft and is considered as a lifting equipment. Boeing 737 is a domestic aircraft considered for designing this bag. The aircraft lift bag is made of composite material, and the most widely used materials are nylon and neoprene. A composite material is used to make the bag lightweight and easy to handle. For calculation of properties and the engineering constant of the respective composite materials, micromechanics approach is used, in which the method of Representative Volume Element (RVE) is taken into consideration. The loading and boundary conditions are the exact replica of the working conditions. The operation of this bag is completely pneumatic. The stresses induced in the bag are analyzed in finite element software and are compared with the calculated theoretical values. CATIA is used to model the bag, and ABAQUS is used for the finite element calculations.

Abstract This article addresses the design, testing, and evaluation of rigorous and verifiable prognostic and health management (PHM) functions applied to autonomous aircraft systems. These PHM functions—many deployed as algorithms—are integrated into a holistic framework for integrity management of aircraft components and systems that are subject to both operational degradation and incipient failure modes. The designer of a comprehensive and verifiable prognostics system is faced with significant challenges. Data (both baseline and faulted) that are correlated, time stamped, and appropriately sampled are not always readily available. Quantifying uncertainty, and its propagation and management, which are inherent in prognosis, can be difficult. High-fidelity modeling of critical components/systems can consume precious resources. Data mining tools for feature extraction and selection are not easy to develop and maintain.

Abstract This article reports a reduced-order modeling framework of bladed disks on a rotating shaft to simulate the vibration signature of faults in different components, aiming toward simulated data-driven machine learning. We have employed lumped and one-dimensional analytical models of the subcomponents for better insight into the complex dynamic response. The framework addresses some of the challenges encountered in analyzing and optimizing fault detection and identification schemes for health monitoring of aeroengines and other rotating machinery. We model the bladed disks and shafts by combining lumped elements and one-dimensional finite elements, leading to a coupled system. The simulation results are in good agreement with previously published data. We model and analyze the cracks in a blade with their effective reduced stiffness approximation.